Survey on Early Detection of Alzheimer's Disease using Different Types of Neural Network Architecture

  • Deepthi Kamath
  • Misba Firdose Fathima
  • Monica K. P.
  • M. Kusuma
Keywords: Alzheimer’s Disease, Convolutional Neural Network, Magnetic Resonance Imaging

Abstract

Alzheimer’s disease is a condition that leads to, progressive neurological brain disorder and destroys cells of the brain thereby causing an individual to lose their ability to continue daily activities and also hampers their mentality. Diagnostic symptoms are experienced by patients usually at later stages after irreversible neural damage occurs. Detection of AD is challenging because sometimes the signs that distinguish AD MRI data, can be found in MRI data of normal healthy brains of older people. Even though this disease is not completely curable, earlier detection can aid in promising treatment and prevent permanent damage to brain tissues. Age and genetics are the greatest risk factors for this disease. This paper presents the latest reports on AD detection based on different types of Neural Network Architectures.

Downloads

Download data is not yet available.

Author Biographies

Deepthi Kamath

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore. Karnataka, India.

Misba Firdose Fathima

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore. Karnataka, India.

Monica K. P.

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore. Karnataka, India.

M. Kusuma

Department of Information Science and Engineering. Dayananda Sagar Academy of Technology and Management, Bangalore. Karnataka, India.

This is an open access article, licensed under CC-BY-SA

Creative Commons License
Published
        Views : 211
2021-06-22
    Downloads : 130
How to Cite
[1]
D. Kamath, M. F. Fathima, M. K. P., and M. Kusuma, “Survey on Early Detection of Alzheimer’s Disease using Different Types of Neural Network Architecture”, International Journal of Artificial Intelligence, vol. 8, no. 1, pp. 25-32, Jun. 2021.
Section
Articles

References

Anonymous, “Stages of Alzheimer's”, Alzheimer's Association”, 2021. [Online]. Available: https:// www.alz.org/alzheimers-dementia/stages. [Accessed: January, 2021].

Anonymous, “Alzheimer's Medical Illustration of the Symptoms”, 2021. [Online]. Available: https:// st2.depositphotos.com/2852841/12083/v/950/depositphotos_120837382-stock-illustrati on-medical-illustration-of-the-symptoms.jpg. [Accessed: January, 2021].

I. Jyoti, and Y. Zhang, “Ensemble of Deep Convolutional Neural Networks for Alzheimer's Disease Detection and Classification,” arXiv preprint arXiv:1712.01675, 2017.

Report 2020, “2020 Alzheimer's Disease Facts and Figures”, the Journal of Alzheimer's Association. https:// doi.org/10.1002/alz.12068, 2020.

A. Pai, “CNN vs. RNN vs. ANN: an Analyzing 3 Types of Neural Networks in Deep Learning, February”, 17, 2020. [Online]. Available: https:// www.analyticsvidhya.com/blog/2020/02/cnn-vs-rnn-vs-mlp-analyzing-3-types-of-neural-networks-in-deep-learning/ [Accessed: January, 2021].

Anonymous, “Artificial Neural Network”, February, 2021. [Online]. Available: https:// images.squarespace-cdn.com/content/v1/5ccb715016b640627a1c2782/ [Accessed: January, 2021].

Anonymous, “Analyticsvidhya”, 2021. [Online]. Available: https:// cdn.analyticsvidhya.com/wp-content/uploads/2020/02/1d_POV7c8fzHbKuTgJzCxtA.gif [Accessed: January, 2021].

Anonymous, “Analyticsvidhya”, 2021. [Online]. Available: https:// cdn.analyticsvidhya.com/wp-content/uploads/2020/02/1oB3S5yHHhvougJkPXuc8og.gif [Accessed: January, 2021].

Anonymous, “Deep Learning, 2021. [Online]. Available: https:// en.wikipedia.org/wiki/Deep_learning [Accessed: January, 2021].

I. Goodfellow, Y. Bengio, A. Courville, and Y. Bengio, Deep learning. Cambridge: MIT Press, 2016.

Anonymous, “Deep Learn”, 2021. [Online]. Available: https:// orbograph.com/wp-content/uploads/2019/01/DeepLearn.png [Accessed: January, 2021].

J. Taeho, K. Nho, and A. J. Saykin, “Deep learning in Alzheimer’s disease: diagnostic classification and prognostic prediction using neuroimaging data”, Frontiers in Aging Neuroscience, vol. 11, pp. 220, 2019.

E. Yagis, L. Citi, S. Diciotti, C. Merzi, W. S. Atnafu, and G. S De Herrera, “ 3D Convolutional Neural Networks for Diagnosis of Alzheimer’s Disease via structural MRI”, 2020.

Y. Huang, J. Xu, Y. Zhou, T. Tong, and X. Zhuang, “Alzheimer’s Disease Neuroimaging Initiative (ADNI)”, Diagnosis of Alzheimer’s disease via multi-modality 3D convolutional neural network. Frontiers in Neuroscience, vol. 13, pp. 509, 2019.

A. Mehmood, M. Maqsood, M. Bashir, and Y. Shuyuan, “A Deep Siamese Convolution Neural Network for Multi-Class Classification of Alzheimer Disease. Brain Sciences”, vol. 10, no. 2, pp. 84, 2020.

S. Saman, and G. Tofighi. “Classification of alzheimer's disease structural MRI data by deep learning convolutional neural networks”, arXiv preprint arXiv:1607.06583,2016.

F. Al-Azdi, R. Passarella, A. Susanto, C. Caroline, R. D. Puspa and T. W. Yudha, “Design of A Convolutional Neural Network System to Increase Diagnostic Efficiency of Alzheimer’s Disease”, in IOP Conference Series: Materials Science and Engineering, IOP Publishing, vol. 648, no. 1, October , 2019.

A. Farooq, S. Anwar, M. Awais, and S. Rehman, “A deep CNN based multi-class classification of Alzheimer's disease using MRI ”, in IEEE International Conference on Imaging systems and Techniques (IST) IEEE, pp. 1-6, October 2017.

S. Saman, and G. Tofighi. “Alzheimer’s Disease Neuroimaging Initiative”, DeepAD: Alzheimer’s disease classification via dkr7eeep convolutional neural networks using MRI and fMRI. BioRxiv, pp. 070441, 2016.